Abstract
Conventional studies on rule discovery and rough set methods mainly focus on acquisition of rules, the targets of which have mutually exclusive supporting sets. However, mutual exclusiveness does not always hold in real-world databases, where conventional probabilstic approaches cannot be applied. In this paper, first, we show that these phenomena are easily found in data mining contexts: when we apply attribute-oriented generalization to attributes in databases, generalized attributes will have fuzziness for classification. Secondly, we show that real-world databases may have fuzzy contexts. Then, finally, these contexts should be analyzed by using fuzzy techniques, where context-free fuzzy sets will be a key idea.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cai, Y.D., Cercone, N., Han, J.: Attribute-oriented induction in relational databases. In: Shapiro, G.P., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 213–228. AAAI press, Palo Alto (1991)
Fayyad, U.M., et al. (eds.): Advances in Knoweledge Discovery and Data Mining. AAAI Press, Menlo Park (1996)
Lin, T.Y.: Fuzzy Partitions: Rough Set Theory. In: Proceedings of IPMU 1998, Paris, pp. 1167–1174 (1998)
Lin, T.Y.: Context Free Fuzzy Sets and Information Tables. In: Proceedings of EUFIT 1998, Aachen, pp. 76–80 (1998)
Pawlak, Z.: Rough Sets. Kluwer Academic Publishers, Dordrecht (1991)
Pawlak, Z.: Conflict analysis. In: Proceedings of the Fifth European Congress on Intelligent Techniques and Soft Computing (EUFIT 1997), pp. 1589–1591. Verlag Mainz, Aachen (1997)
Pawlak, Z.: Rough Modus Ponens. In: Proceedings of IPMU 1998, Paris (1998)
Pawlak, Z.: Rough Sets and Decision Analysis. In: Fifth IIASA workshop on Decision Analysis and Support, Laxenburg (1998)
Skowron, A., Grzymala-Busse, J.: From rough set theory to evidence theory. In: Yager, R., Fedrizzi, M., Kacprzyk, J. (eds.) Advances in the Dempster-Shafer Theory of Evidence, pp. 193–236. John Wiley & Sons, New York (1994)
Shavlik, J.W., Dietterich, T.G. (eds.): Readings in Machine Learning. Morgan Kaufmann, Palo Alto (1990)
Tsumoto, S.: Knowledge Discovery in Medical Databases based on Rough Sets and Attribute-Oriented Generalization. In: Proceedings of IEEE-FUZZ 1999. IEEE Press, Anchorage (1998)
Tsumoto, S.: Automated Induction of Medical Expert System Rules from Clinical Databases based on Rough Set Theory. Information Sciences 112, 67–84 (1998)
Tsumoto, S.: Automated discovery of plausible rules based on rough sets and rough inclusion. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 210–219. Springer, Heidelberg (1999)
Zadeh, L.A.: Toward a theory of fuzzy information granulation and its certainty in human reasoning and fuzzy logic. Fuzzy Sets and Systems 90, 111–127 (1997)
Ziarko, W.: Variable Precision Rough Set Model. Journal of Computer and System Sciences 46, 39–59 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tsumoto, S., Lin, T.Y. (1999). Context-Free Fuzzy Sets in Data Mining Context. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-540-48061-7_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66645-5
Online ISBN: 978-3-540-48061-7
eBook Packages: Springer Book Archive